An XLSR-Conformer system trained with Real Emphasis, Fake Dispersion, and multi-class N-pair loss reaches 95.6% in-domain and up to 44.8% out-of-domain source tracing accuracy on MLAAD, versus 83.4% and 26.5% for the Wav2Vec2-AASIST baseline.
PFFS-21-47), by the Swiss National Science Foundation through the project PASS: Pathological Speech Synthesis (grant agreement no
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Unveiling Audio Deepfake Origins: A Deep Metric learning And Conformer Network Approach With Ensemble Fusion
An XLSR-Conformer system trained with Real Emphasis, Fake Dispersion, and multi-class N-pair loss reaches 95.6% in-domain and up to 44.8% out-of-domain source tracing accuracy on MLAAD, versus 83.4% and 26.5% for the Wav2Vec2-AASIST baseline.